Low back pain (LBP) is a prevalent global health concern, leading to substantial disability and socioeconomic burden. A significant proportion of LBP cases progress into chronic LBP (cLBP), posing challenges for healthcare systems. Existing literature highlights the substantial impact of LBP on global disability rates, work absenteeism, and healthcare costs. The prevalence of LBP has been estimated at 7.5% of the global population, affecting millions of individuals worldwide. The multifactorial etiology of LBP complicates clinical diagnosis and treatment. Chronic LBP, which affects 10%-15% of cases, significantly impairs patient functioning and further exacerbates healthcare costs. To address the complexity of cLBP, a multidimensional approach is necessary, considering physical, psychological, social, and occupational factors. Patient education and exercise therapy are key components of cLBP rehabilitation; however, the realization of effective precision spine care remains limited. Leveraging digital platforms, wearable sensors, and artificial intelligence presents an innovative solution to enable a tailored and integrated approach to cLBP diagnosis and rehabilitation. These technologies have the potential to revolutionize the delivery of care, providing personalized interventions and improving patient outcomes. Physical rehabilitation is a cornerstone in cLBP management, and the integration of digital health interventions offers promising opportunities for improved outcomes and personalized spine care. This research project aims to address the limitations of current approaches by developing and optimizing an eHealth system for evaluating and delivering rehabilitation exercises tailored to cLBP. Additionally, a quantitative assessment system based on wearable sensors and artificial intelligence will be developed and validated to assess lumbar kinematics and dynamics, enabling diagnosis, monitoring, and follow-up of cLBP. Furthermore, the project aims to evaluate the effectiveness of an eHealth-based rehabilitation protocol in a randomized clinical trial (RCT) involving cLBP patients. In conclusion, this research project aims to optimize an eHealth system, develop a quantitative assessment system using wearable sensors and artificial intelligence, and evaluate the effectiveness of an eHealth-based rehabilitation protocol for cLBP. The findings from this study have the potential to enhance rehabilitation outcomes, foster personalized spine care, and contribute to the advancement of precision medicine in the management of cLBP.